Opioid abuse and austerity: Evidence on health service use and mortality in England
Bibliographic record
Abstract
Opioid abuse has become a public health concern among many developed countries, with policymakers searching for strategies to mitigate adverse effects on population health and the wider economy. The United Kingdom has seen dramatic increases in opioid-related mortality following the financial crises in 2008. We examine the impact of spending cuts resulting from government prescribed austerity measures on opioid-related hospitalisations and mortality, thereby expanding on existing evidence suggesting a countercyclical relationship with macroeconomic performance. We take advantage of the variation in spending cuts passed down from central government to local authorities since 2010, with reductions in budgets of up to fifty percent in some areas resulting in the rescaling of vital public services. Longitudinal panel data methods are used to analyse a comprehensive, linked dataset that combines information from spending records, official death registry data and large administrative health care data for 152 local authorities (i.e., unitary authorities and county councils) in England between April 2010 and March 2017. A total of 280,827 people experienced a hospital admission in the English National Health Service because of an opioid overdose and 14,700 people died from opioids across the study period. Local authorities that experienced largest spending cuts also saw largest increases in opioid abuse. Interactions between changes in unemployment and spending items for welfare programmes show evidence about the importance for governments to protect populations from social-risk effects at times of deteriorating macroeconomic performance. Our study carries important lessons for countries aiming to address high rates of opioid abuse, including the United States, Canada and Sweden.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".